{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User testing for for Scikit-Yellowbrick\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using data that was recorded from sensors during Data Science Certificate Program at GW\n",
    "https://github.com/georgetown-analytics/classroom-occupancy \n",
    "\n",
    "Data consist of temperature, humidity, CO2 levels, light, # of bluetooth devices, noise levels and count of people in the room."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>loc_CO2</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>co2</th>\n",
       "      <th>light</th>\n",
       "      <th>noise</th>\n",
       "      <th>bluetooth_devices</th>\n",
       "      <th>bluetooth_non_personal_devices</th>\n",
       "      <th>count_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-03-25 09:05:58</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-03-25 09:06:21</td>\n",
       "      <td>Georgetown-manual</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "              datetime            loc_CO2  temperature  humidity    co2  \\\n",
       "0  2017-03-25 09:05:58  Georgetown-manual         22.6      36.9  781.0   \n",
       "1  2017-03-25 09:06:04  Georgetown-manual         23.8      39.0  767.0   \n",
       "2  2017-03-25 09:06:10  Georgetown-manual         23.8      39.0  754.0   \n",
       "3  2017-03-25 09:06:15  Georgetown-manual         23.8      39.0  768.0   \n",
       "4  2017-03-25 09:06:21  Georgetown-manual         23.8      39.0  758.0   \n",
       "\n",
       "   light  noise  bluetooth_devices  bluetooth_non_personal_devices  \\\n",
       "0  430.0  511.0                1.0                             NaN   \n",
       "1  448.0  510.0                8.0                             NaN   \n",
       "2  423.0  511.0                8.0                             NaN   \n",
       "3  412.0  492.0                8.0                             NaN   \n",
       "4  428.0  491.0                9.0                             NaN   \n",
       "\n",
       "   count_total  \n",
       "0          0.0  \n",
       "1          0.0  \n",
       "2          0.0  \n",
       "3          0.0  \n",
       "4          0.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    17962.000000\n",
       "mean        24.647812\n",
       "std         10.213500\n",
       "min          0.000000\n",
       "25%         25.000000\n",
       "50%         29.000000\n",
       "75%         31.000000\n",
       "max         31.000000\n",
       "Name: count_total, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.count_total.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#add a new column to create a binary class for room occupancy \n",
    "countmed = dataset.count_total.median()\n",
    "dataset['room_occupancy'] = dataset['count_total'].apply(lambda x: 'occupied' if x > 4 else 'empty')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# map room occupancy to a number\n",
    "dataset['room_occupancy_num'] = dataset.room_occupancy.map({'empty':0, 'occupied':1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
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       "      <th>datetime</th>\n",
       "      <th>loc_CO2</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>co2</th>\n",
       "      <th>light</th>\n",
       "      <th>noise</th>\n",
       "      <th>bluetooth_devices</th>\n",
       "      <th>bluetooth_non_personal_devices</th>\n",
       "      <th>count_total</th>\n",
       "      <th>room_occupancy</th>\n",
       "      <th>room_occupancy_num</th>\n",
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       "  </thead>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              datetime            loc_CO2  temperature  humidity    co2  \\\n",
       "0  2017-03-25 09:05:58  Georgetown-manual         22.6      36.9  781.0   \n",
       "1  2017-03-25 09:06:04  Georgetown-manual         23.8      39.0  767.0   \n",
       "2  2017-03-25 09:06:10  Georgetown-manual         23.8      39.0  754.0   \n",
       "3  2017-03-25 09:06:15  Georgetown-manual         23.8      39.0  768.0   \n",
       "4  2017-03-25 09:06:21  Georgetown-manual         23.8      39.0  758.0   \n",
       "\n",
       "   light  noise  bluetooth_devices  bluetooth_non_personal_devices  \\\n",
       "0  430.0  511.0                1.0                             NaN   \n",
       "1  448.0  510.0                8.0                             NaN   \n",
       "2  423.0  511.0                8.0                             NaN   \n",
       "3  412.0  492.0                8.0                             NaN   \n",
       "4  428.0  491.0                9.0                             NaN   \n",
       "\n",
       "   count_total room_occupancy  room_occupancy_num  \n",
       "0          0.0          empty                   0  \n",
       "1          0.0          empty                   0  \n",
       "2          0.0          empty                   0  \n",
       "3          0.0          empty                   0  \n",
       "4          0.0          empty                   0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        17962\n",
       "unique           2\n",
       "top       occupied\n",
       "freq         15859\n",
       "Name: room_occupancy, dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.room_occupancy.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys \n",
    "\n",
    "# Modify the path \n",
    "sys.path.append(\"..\")\n",
    "\n",
    "import pandas as pd\n",
    "import yellowbrick as yb \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['figure.figsize'] = (12, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6BpzsmubMpdEbPvekt5b3veP4NvQyOTMzM/h8vkx3IyNyfcwG+/y5ytY6njev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\nERERiQAF2iIiIiIiEfD/AGGNle6mudq4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b526550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = yb.anscombe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "raw_mimetype": "text/html"
   },
   "source": [
    "Feature analysis visualizers are designed to visualize instances in data space in order to detect features or targets that might impact downstream fitting. Because ML operates on high-dimensional data sets (usually at least 35), the visualizers focus on aggregation, optimization, and other techniques to give overviews of the data. It is our intent that the steering process will allow the data scientist to zoom and filter and explore the relationships between their instances and between dimensions.\n",
    "\n",
    "At the moment we have three feature analysis visualizers implemented:\n",
    "\n",
    "Rank2D: rank pairs of features to detect covariance\n",
    "\n",
    "RadViz: plot data points along axes ordered around a circle to detect separability\n",
    "\n",
    "Parallel Coordinates: plot instances as lines along vertical axes to detect clusters\n",
    "\n",
    "Feature analysis visualizers implement the Transformer API from Scikit-Learn, meaning they can be used as intermediate transform steps in a Pipeline (particularly a VisualPipeline). They are instantiated in the same way, and then fit and transform are called on them, which draws the instances correctly. Finally show or show is called which displays the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from yellowbrick.features.rankd import Rank2D \n",
    "from yellowbrick.features.radviz import RadViz \n",
    "from yellowbrick.features.pcoords import ParallelCoordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rank2D\n",
    "\n",
    "Rank1D and Rank2D evaluate single features or pairs of features using a variety of metrics that score the features on the scale [-1, 1] or [0, 1] allowing them to be ranked. A similar concept to SPLOMs, the scores are visualized on a lower-left triangle heatmap so that patterns between pairs of features can be easily discerned for downstream analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load the classification data set\n",
    "data = dataset\n",
    "\n",
    "# Specify the features of interest\n",
    "features = ['temperature','humidity','co2','light','noise','bluetooth_devices']\n",
    "\n",
    "# Extract the numpy arrays from the data frame\n",
    "X = data[features].as_matrix()\n",
    "y = data['count_total'].as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GljJzWURsTYmBZgD/mJn3TuaFJUnSBAx2aGlb4Nqebdls72136SMNMn8XEXcB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b1c6198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the visualizer with the Covariance ranking algorithm \n",
    "visualizer = Rank2D(features=features, algorithm='covariance')\n",
    "\n",
    "visualizer.fit(X, y)                # Fit the data to the visualizer\n",
    "visualizer.transform(X)             # Transform the data\n",
    "visualizer.show()                   # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DOEZmbA72lSbP6tWrueaaawC223nnnW/oZ1tWrrp3Uv5DnDN7VtVrH7jWkiRJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b1c04a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the visualizer with the Pearson ranking algorithm\n",
    "visualizer = Rank2D(features=features, algorithm='pearson')\n",
    "\n",
    "visualizer.fit(X, y)                # Fit the data to the visualizer\n",
    "visualizer.transform(X)             # Transform the data\n",
    "visualizer.show()                   # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RadViz\n",
    "\n",
    "RadViz is a multivariate data visualization algorithm that plots each feature dimension uniformely around the circumference of a circle then plots points on the interior of the circle such that the point normalizes its values on the axes from the center to each arc. This meachanism allows as many dimensions as will easily fit on a circle, greatly expanding the dimensionality of the visualization.\n",
    "Data scientists use this method to dect separability between classes. E.g. is there an opportunity to learn from the feature set or is there just too much noise?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Specify the features of interest and the classes of the target \n",
    "features = ['temperature','humidity','co2','light','noise','bluetooth_devices']\n",
    "classes = ['empty', 'occupied']\n",
    "\n",
    "# Extract the numpy arrays from the data frame \n",
    "X = data[features].as_matrix()\n",
    "y = data.room_occupancy_num.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V1XXkLV3K4/E0+SiBo7EIHhqL4KGxCB4ai+ChsQgOwTwOXq+31WNuQ3U/oLxZ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\nH2dTjkl3PwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b42e2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the visualizer\n",
    "visualizer = visualizer = RadViz(classes=classes, features=features)\n",
    "\n",
    "visualizer.fit(X, y)      # Fit the data to the visualizer\n",
    "visualizer.transform(X)   # Transform the data\n",
    "visualizer.show()         # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For regression, the RadViz visualizer should use a color sequence to display the target information, as opposed to discrete colors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parallel Coordinates\n",
    "\n",
    "### !!! On this step notebook crashes and has to be restarted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Specify the features of interest and the classes of the target \n",
    "#features = ['temperature','humidity','co2','light','noise','bluetooth_devices']\n",
    "#classes = ['empty', 'occupied']\n",
    "\n",
    "# Extract the numpy arrays from the data frame \n",
    "#X = data[features].as_matrix()\n",
    "#y = data.room_occupancy_num.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Instantiate the visualizer\n",
    "#visualizer = visualizer = ParallelCoordinates(classes=classes, features=features)\n",
    "\n",
    "#visualizer.fit(X, y)      # Fit the data to the visualizer\n",
    "#visualizer.transform(X)   # Transform the data\n",
    "#visualizer.show()         # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Regressor Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regression models attempt to predict a target in a continuous space. Regressor score visualizers display the instances in model space to better understand how the model is making predictions. We currently have implemented two regressor evaluations:\n",
    "\n",
    "Residuals Plot: plot the difference between the expected and actual values\n",
    "\n",
    "Prediction Error: plot expected vs. the actual values in model space\n",
    "\n",
    "Estimator score visualizers wrap Scikit-Learn estimators and expose the Estimator API such that they have fit(), predict(), and score() methods that call the appropriate estimator methods under the hood. Score visualizers can wrap an estimator and be passed in as the final step in a Pipeline or VisualPipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Regression Evaluation Imports \n",
    "\n",
    "from sklearn.linear_model import Ridge, Lasso \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from yellowbrick.regressor import PredictionError, ResidualsPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Residuals Plot\n",
    "\n",
    "A residual plot shows the residuals on the vertical axis and the independent variable on the horizontal axis. If the points are randomly dispersed around the horizontal axis, a linear regression model is appropriate for the data; otherwise, a non-linear model is more appropriate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the data\n",
    "df = data\n",
    "feature_names = ['temperature','humidity','co2','light','noise','bluetooth_devices']\n",
    "target_name = 'count_total'\n",
    "\n",
    "# Get the X and y data from the DataFrame \n",
    "X = df[feature_names].as_matrix()\n",
    "y = df[target_name].as_matrix() \n",
    "\n",
    "# Create the train and test data \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DH/6QG2+8kVWrVvHQQw/xzDPPsGLFCi655BLOPvtsLrroIn73u9/1uK+IiIhIrlDS3UfL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O23G2moe+pHQ99Alp5MPdAz4vEPP5mmO1PL/PQg+wZS7OxeztOXZEnX3jNkxTufh4FcM2tP/EPW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\nJEmqMEO3JEmSVGGGbkmSJKnC/j9ZcdUZFWb/CwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bce4898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the linear model and visualizer \n",
    "ridge = Ridge()\n",
    "visualizer = ResidualsPlot(ridge)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data \n",
    "g = visualizer.show()             # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction Error Plot\n",
    "\n",
    "Plots the actual targets from the dataset against the predicted values generated by our model. This allows us to see how much variance is in the model. Data scientists diagnose this plot by comparing against the 45 degree line, where the prediction exactly matches the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the data\n",
    "df = data\n",
    "feature_names = ['temperature','humidity','co2','light','noise','bluetooth_devices']\n",
    "target_name = 'count_total'\n",
    "\n",
    "# Get the X and y data from the DataFrame \n",
    "X = df[feature_names].as_matrix()\n",
    "y = df[target_name].as_matrix() \n",
    "\n",
    "# Create the train and test data \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eeqNxD+2t4zNXXRRzXFaGWcJEdqbZzHJrT7/RuJazZuOcUvqHiIiM4maJKxhT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\noFpEREREJEb/H/B7IY9rnENdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bf87860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the linear model and visualizer \n",
    "lasso = Lasso()\n",
    "visualizer = PredictionError(lasso)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data \n",
    "g = visualizer.show()             # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifier Evaluation\n",
    "\n",
    "Classification models attempt to predict a target in a discrete space, that is assign an instance of dependent variables one or more categories. Classification score visualizers display the differences between classes as well as a number of classifier-specific visual evaluations. We currently have implemented three classifier evaluations:\n",
    "\n",
    "ClassificationReport: Presents the confusion matrix of the classifier as a heatmap\n",
    "\n",
    "ROCAUC: Presents the graph of receiver operating characteristics along with area under the curve\n",
    "\n",
    "ClassBalance: Displays the difference between the class balances and support\n",
    "\n",
    "Estimator score visualizers wrap Scikit-Learn estimators and expose the Estimator API such that they have fit(), predict(), and score() methods that call the appropriate estimator methods under the hood. Score visualizers can wrap an estimator and be passed in as the final step in a Pipeline or VisualPipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Classifier Evaluation Imports \n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from yellowbrick.classifier import ClassificationReport, ROCAUC, ClassBalance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification report\n",
    "\n",
    "The classification report visualizer displays the precision, recall, and F1 scores for the model. Integrates numerical scores as well color-coded heatmap in order for easy interpretation and detection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the classification data set\n",
    "data = dataset\n",
    "\n",
    "# Specify the features of interest and the classes of the target \n",
    "features = ['temperature','humidity','co2','light','noise','bluetooth_devices']\n",
    "classes = ['empty', 'occupied']\n",
    "\n",
    "# Extract the numpy arrays from the data frame \n",
    "X = data[features].as_matrix()\n",
    "y = data.room_occupancy_num.as_matrix()\n",
    "\n",
    "# Create the train and test data \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SD3cF8KcQwnDSc+Qc0oWRNR+XeaQx46/EGN+utZtrSb2zT4QQriRdeHcxaaq+u9a62Po5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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119dd82b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the classification model and visualizer \n",
    "bayes = GaussianNB()\n",
    "visualizer = ClassificationReport(bayes, classes=classes)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data \n",
    "g = visualizer.show()             # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ROCAUC\n",
    "\n",
    "Plot the ROC to visualize the tradeoff between the classifier's sensitivity and specificity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RERER8QrZXcIxGHgZu12+lEeBhcaYn40x8cBAoJ1lWeUuc0YREREREa+R3SUc7wFjgVv+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119f54550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the classification model and visualizer \n",
    "logistic = LogisticRegression()\n",
    "visualizer = ROCAUC(logistic)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data \n",
    "g = visualizer.show()             # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ClassBalance\n",
    "\n",
    "Class balance chart that shows the support for each class in the fitted classification model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QYA9gO+CpwPOAI5ptbgecBewFbAXcCpyz2o5KkiRJWsN6GdqxDfCNzDwvM+/L\nzB9TepCfC+wJfCAzl2TmtcB5wH7NevsCp2Xmwsy8FTgJ2L8p2we4KDPnZuZi4Chgt4iYvNqOTJIk\nSVqDxhzakZnXU0IxcH8P9QuAnwJLM/PmzurAa5u/pwE3dpVFM+RjGnBNxz5uj4g7gAB6Gt4xNDTE\nyMhIL1W1ARsaGnrAb0mStOFZsmRJX/Y7PDy80vKexki3RcQjgUuA6yi90od1VbkHmNT8vXlzu7Ns\nI6A1Sln3umNasGBBz+3Whm9wcLDfTZAkSWvI/Pnz+92EUfUcpCPiKcDXgV8Bb6KMe57YVW0SZcw0\nlGC8WVfZvZm5JCK6y7rXHdPUqVOZMGFCr9W1gRoaGmJwcJApU6bQarX63RxJGrefzbug302Q1nnT\np0/vy36Hh4dX2nnb66wdOwDfAmYD783M+yJiATAhIp6Umb9tV2X5cI6bmttzO8pu6iprb38rYMuO\n8jG1Wi2Dk+7XarWYOLH7c50kSdoQ9Os9fmBgYKXlYwbp5gTAbwGnZObH2ssz8+6IuIgyg8fBwHRg\nb2D3psps4MiIuBxYChwDnNuUnQ9cFRGzgHmUExEvzczbx3FskiRJUt/00iN9IPAY4ISIOKFj+enA\nwZRp7H5PGZZxZGa2e6DPpEyLdy1lXPRs4FQoJzA24XsW8Djge8ABq3w0kiRJ0lrSy6wdMykXVlmR\nN65gvWXA8c3PaOVzgDk9tFGSJEla53iJcEmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmC\nQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqS\nJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmq\nYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCW\nJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmS\nKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhik\nJUmSpAoGaUmSJKmCQVqSJEmqYJCWJEmSKhikJUmSpAqbjKdyRDwT+Fpmbt3c3gmYCyzuqDYzM2dG\nxAAwEzhN9yFkAAARuElEQVSo2c8XgMMzc1mz7l7AicBk4ArgwMz80yoejyRJkrRW9BSkm1B8AHAq\ncG9H0fbApZn5ilFWOwTYA9gOGAG+DhwBnBwR2wFnAbsCPwXOAM4Bdq87DEmSJGnt6nVox7HAYZQe\n5E7bA9evYJ19gdMyc2Fm3gqcBOzflO0DXJSZczNzMXAUsFtETB5P4yVJkqR+6XVoxyzKMI0XdS3f\nHlgSEb8GNgbmAMdl5hAwDbixo24C0fRuTwOuub8g8/aIuAMIoKfhHUNDQ4yMjPTYfG2ohoaGHvBb\nkiRteJYsWdKX/Q4PD6+0vKcgnZkLASKiu+gvwJXA2ZSxzhcAM4Cjgc2Bezrq3kPpAW+NUtYun9RL\newAWLFjQa1U9BAwODva7CZIkaQ2ZP39+v5swqnGdbNgtM1/VcfPmiJhJ6bk+mhKMN+sonwTcm5lL\nIqK7rF2+qNd9T506lQkTJtQ1XBuMoaEhBgcHmTJlCq1Wq9/NkaRx+9m8C/rdBGmdN3369L7sd3h4\neKWdt9VBOiK2AI4DZmTm3c3iiUC77/0mylCNue1VmmWdZe1tbQVs2VE+plarZXDS/VqtFhMnTux3\nMyRJ0hrQr/f4gYGBlZavSo/0XcBrgIGIOBrYhhKsP9OUzwaOjIjLgaXAMcC5Tdn5wFURMQuYRzkR\n8dLMvH0V2iNJkiStNdUXZMnM+4BXAk8HbgO+TxkjfXpT5UzgIuBaykmHV1OmzyMzrwcOppzE+Gdg\na8r0epIkSdJ6YVw90pl5JbBVx+0bgV1WUHcZcHzzM1r5HMosH5IkSdJ6x0uES5IkSRUM0pIkSVIF\ng7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7Qk\nSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJU\nwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAt\nSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIk\nVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBI\nS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVTBIS5IkSRUM0pIkSVIFg7QkSZJUwSAtSZIkVdhkPJUj\n4pnA1zJz6+b2FsAs4CXAXcCMzPxcUzYAzAQOavbzBeDwzFzWlO8FnAhMBq4ADszMP62Og5IkSZLW\ntJ56pCNiICL+FfgOMKGj6LPAIkoYfj1wckQ8uyk7BNgD2A54KvA84Ihme9sBZwF7AVsBtwLnrOrB\nSJIkSWtLr0M7jgUOo/QgAxARDwP2BD6QmUsy81rgPGC/psq+wGmZuTAzbwVOAvZvyvYBLsrMuZm5\nGDgK2C0iJq/qAUmSJElrQ69DO2ZRhmm8qGPZVGBpZt7csSyB1zZ/TwNu7CqLZsjHNOCa+wsyb4+I\nO4AAehreMTQ0xMjISI/N14ZqaGjoAb8lSdKGZ8mSJX3Z7/Dw8ErLewrSmbkQICI6F28OLO6qeg8w\nqaP8nq6yjYDWKGXd645pwYIFvVbVQ8Dg4GC/myBJktaQ+fPn97sJoxrXyYZd7gEmdi2bRBkz3S7f\nrKvs3sxcEhHdZd3rjmnq1KlMmDBh7IraoA0NDTE4OMiUKVNotVr9bo4kjdvP5l3Q7yZI67zp06f3\nZb/Dw8Mr7bxdlSC9AJgQEU/KzN82y4Llwzluam7P7Si7qausFERsBWzZUT6mVqtlcNL9Wq0WEyd2\nf66TJEkbgn69xw8MDKy0vDpIZ+bdEXERcFJEHAxMB/YGdm+qzAaOjIjLgaXAMcC5Tdn5wFURMQuY\nRzkR8dLMvL22PZIkSdLatKoXZDkY2BT4PXAhcGRmtnugzwQuAq6l9FJfDZwKkJnXN+vOAv4MbA0c\nsIptkSRJktaacfVIZ+aVlHmf27fvAN64grrLgOObn9HK5wBzxrN/SZIkaV3hJcIlSZKkCgZpSZIk\nqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJB\nWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIk\nSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapg\nkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYk\nSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIq\nGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqYJBWpIkSapgkJYkSZIqbLKq\nG4iI9wIzgeGOxS8Hfg7MAl4C3AXMyMzPNesMNOsc1LThC8DhmblsVdsjSZIkrQ2rHKSB7YFjM/MT\nnQsj4ivAImAysB1waUTMz8wfAocAezTLR4CvA0cAJ6+G9kiSJElr3OoK0ud0LoiIhwF7Attm5hLg\n2og4D9gP+CGwL3BaZi5s6p8EfJhxBOmhoSFGRkZWQ/O1PhsaGnrAb0mStOFZsmRJX/Y7PDy80vJV\nCtIRMQkI4LCImA38Ffg48BNgaWbe3FE9gdc2f08Dbuwqi4gYyMye0vGCBQtWpenawAwODva7CZIk\naQ2ZP39+v5swqlXtkZ4MfB/4NPA64FnAJcApwOKuuvcAk5q/N29ud5ZtBLSAnj5yTJ06lQkTJlQ3\nXBuGoaEhBgcHmTJlCq1Wq9/NkaRx+9m8C/rdBGmdN3369L7sd3h4eKWdt6sUpDPz18CLOhZ9LyLO\nBV4ITOyqPokyZhpKcN6sq+zeZhhIT1qtlsFJ92u1Wkyc2P2UkyRJG4J+vccPDAystHyVpr+LiB0i\n4uiuxROB3wITIuJJndVZPpzjpuZ2Z9lNq9IWSZIkaW1a1aEdi4APRMQg8FXgxcCbKb3UjwJOioiD\ngenA3sDuzXqzgSMj4nJgKXAMcO4qtkWSJElaa1apRzozfwm8EXg/cDdwJnBAZv4YOBjYFPg9cCFw\nZGbObVY9E7gIuJbSS301cOqqtEWSJElam1Z5+rvMvIRygmH38jsoIXu0dZYBxzc/kiRJ0nrHS4RL\nkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUoVVnv7uoWbjI7xuzLrpxrGraK1a\ndsq+/W6CJElrlD3SkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRV\nMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhL\nkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJ\nFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzS\nkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJ\nUgWDtCRJklTBIC1JkiRVMEhLkiRJFQzSkiRJUoVN+rXjiNgeOBuYDiwA/i0zf9iv9kiSJEnj0Zce\n6YiYCFwCnAM8CvgkcHFEPKwf7ZEkSZLGq1890i8G7svMTze3Z0XEe4DdgTljrLsxwKJFixgaGlqD\nTRzd4zffdK3vU1of/e1vf+t3E6T1wqYDk/rdBGmd16/3lKVLl7b/3Hi08n4F6WnAjV3Lslk+lscD\n/OY3v1nNTerNRa+e2pf9SuubBQsW9LsJ0nph2sQ9+t0EaZ23DrynPB74VffCfgXpzYF7upbdA/Ty\nsfxHwAuAhcCy1dwuSZIkqW1jSoj+0WiF/QrS9wCbdS2bBCwaa8Udd9xxCPj+mmiUJEmS1OVBPdFt\n/Zr+7iYgupYFDx7uIUmSJK2T+tUjfTnQioh3AmcB+wKTgW/3qT2SJEnSuPSlRzozh4CXA3sBdwDv\nBF6Vmf/Xj/ZIkiRJ4zUwMjLS7zZIkiRJ6x0vES5JkiRVMEhLkiRJFQzSkiRJUgWDtB4SIuIp/W6D\nJGnDEBHHRsS5Fes9LCJGIuLJa6BZ6oN+TX8nrTURcSjwIuAN/W6LJGn9l5kz+90GrRsM0noo2AoY\n6HcjJEmrLiJeBpwEbAvcDBybmd+MiH8C/hPYAfgLcEJmfjEiNgKOB94ObA5cCRyYmbdHxAjwtMz8\nebPtrwA/z8wPRsSVwLXAnpRLRH8HODgz74iIDwL/lJmvj4iNgeOAf6VcpfnrwLsz82/NNt8DHAm0\ngNPW7L2jtc0grb6LiKcBZwD/DPwOOKr5p/gb4D+AQ4GtgXOBi5q6jwFmZeZ7mm2MAEcDh1Oe12dT\n/nG+BjgW2CgirqX8g3tBZr6sY//XAR/LzDlr/GAlSdUiYjpwMbBP83tX4IKIeG5z+3PAy4CnA1c0\n/993Bt4KvAT4DTCL8j6ydw+73K/Zx6+B84EzgTd31TkceC3wAuBO4LPN9t8aEXtQ3oNeCgwCnxn/\nUWtd5hhp9VVEPJzyKX8Opef4ncDsiNi2qfIG4JnAjsBBwPuAnYAXAoc2/1TbXgFMb+rvBbwtMy8E\nZgJfy8xnAucBO0fEY5r9BzAVuGRNHqckabV4M3BZZn41M+/NzG9SAvRnKL3NH8nM4cz8EfB84I+U\n94NPZjEEHAac2OP+zsjMn2bm3TSdMxHR6qpzIDAjM3/X1DsKeEtETATeBJzbbOOepkwbEIO0+m0P\n4M+ZeWbzT/FKSq/z/k35ZzPzr5n5C2Ah8LnMvDMzb2hub9OxraMz87bM/BVwOuWf5wNk5iBwHfC6\nZtFewFczc/EaODZJ0ur1WEqvcqdbKJ0tCzPzvvbCzLy+GV4xGfh9x/LbMnN+j/sb7Pj798AEYMuu\nOk8CvhARd0bEncANwNJm+eOAP3Ts+w/AvT3uW+sBh3ao354E/GPzz6dtE+Crzd93dCxfRvnarO0+\nHvhhsPsf3uNWsM/ZlF6CsyhB+tDxN1uS1Ae/BZ7dtewpwFXA0yNio3aYjoh3UDpO/gA8oV25mcXp\nrZn5Qcr7yISObT26a9tbd/y9DbAYuL2rzkLK2OnLm+1vCvw98CtKj/j9HT4R8VjMXhsUH0z120Lg\nmsx8YXtBRDyR8s/qOmA817DfGvhT8/c2lPHWo/ky8ImIeAnwCODy8TZaktQXXwaOjYg9KUPydgVe\nRRkHPRs4KiI+Tjnh8ETgucAXgWMi4huUUP0hlp+A/kvg1RHxE2AX4DmUUN52aERcRAnPHwG+lJnD\nZVTg/f4b+EBE3ATc1tR7A+VkyHOBCyNiNvBT4KOr767QusChHeq3bwDTImKviNg4Ip4KzKWcJT1e\nMyLi4c346ncBX2iWD1ECMwCZ+RfgMuBUyj/FZat0BJKktaIZnrcn8H7KN5QfB/bOzLnAKylh+DZK\nqD4wM28CzqGcYHgZpYd4U5Z/E/lOyomCdzXLzuva5TWUMdi3ALdS3lu6nQR8D/hhs+9nAq9ohite\nRpmx48Jm/T9S3pO0gRgYGRlPh5+0+kXEDpQpgbYDFgFnZeZHmlk7Ds3Mrzf1Vni7mbXjDMosHZsA\n/5GZJzf1ngZ8G7g7M6NZtjell+IZmTlvrRyoJGm90Ux/95XM/FS/26J1l0FaG4TuuUB7qP88yomL\n09ZsyyRJ6yODtHrhGGk9pETEZsAU4ATgv/rcHEmStB4zSOuh5lHADyhXq/rPPrdFkrSOysyd+90G\nrfsc2iFJkiRVcNYOSZIkqYJBWpIkSapgkJYkSZIqGKQlSZKkCgZpSZIkqcL/B5Dc+YPepK38AAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bac2128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate the classification model and visualizer \n",
    "forest = RandomForestClassifier()\n",
    "visualizer = ClassBalance(forest, classes=classes)\n",
    "\n",
    "visualizer.fit(X_train, y_train)  # Fit the training data to the visualizer\n",
    "visualizer.score(X_test, y_test)  # Evaluate the model on the test data \n",
    "g = visualizer.show()             # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
